Description
This paper aims to show how researchers can develop learning exercises for training
analysts and executives in market segmentation techniques
International Journal of Culture, Tourism and Hospitality Research
Management learning exercise and trainer's note for market segmentation in tourism
Sara Dolnicar
Article information:
To cite this document:
Sara Dolnicar, (2007),"Management learning exercise and trainer's note for market segmentation in
tourism", International J ournal of Culture, Tourism and Hospitality Research, Vol. 1 Iss 4 pp. 289 - 295
Permanent link to this document:http://dx.doi.org/10.1108/17506180710824172
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J uho Antti Pesonen, (2013),"Information and communications technology and market segmentation in
tourism: a review", Tourism Review, Vol. 68 Iss 2 pp. 14-30http://dx.doi.org/10.1108/TR-02-2013-0006
J ohn T. Bowen, (1998),"Market segmentation in hospitality research: no longer a sequential process",
International J ournal of Contemporary Hospitality Management, Vol. 10 Iss 7 pp. 289-296 http://
dx.doi.org/10.1108/09596119810240924
Ute J amrozy, (2007),"Marketing of tourism: a paradigm shift toward sustainability", International
J ournal of Culture, Tourism and Hospitality Research, Vol. 1 Iss 2 pp. 117-130 http://
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Management learning exercise
and trainer’s note for market
segmentation in tourism
Sara Dolnicar
School of Management andMarketing andMarketing ResearchInnovationCentre,
University of Wollongong, Wollongong, Australia
Abstract
Purpose – This paper aims to show how researchers can develop learning exercises for training
analysts and executives in market segmentation techniques.
Design/methodology/approach – The empirical example of a tour operator specializing in
adventure tourism is used as an illustration. Segments are constructed on the basis of tourists’ stated
willingness to pay a price premium for certain aspects of the tour. Stability analysis is conducted to
choose the number of clusters, topology representing networks are used to construct segments and
Bonferroni-corrected x
2
tests provide insight into the external validity of segments.
Findings – Four market segments are constructed which differ signi?cantly with respect to external
variables.
Research limitations/implications – Market segmentation can be used by any entity in the
tourism industry to select a suitable part of the entire market, customize the tourism service to suit
such a segment, and spend marketing budget more ef?ciently by using communication channels and
advertising messages most effective for the selected segment.
Originality/value – Market segmentation provides managers with insight into market structure.
Knowledge about the market structure, in turn, is the basis of successful strategic planning. While the
concept of segmentation is not new, each application is unique to its context. The present paper
focuses on price premium segments in the adventure tourism context.
Keywords Market segmentation, Learning, Training, Tourism management
Paper type Research paper
Smith (1956, p. 6), who introduced the concept of market segmentation to the ?eld of
marketing, provides the following de?nition for market segmentation: “Market
segmentation [. . .] consists of viewing a heterogeneous market (one characterized by
divergent demand) as a number of smaller homogeneous markets.” Market
segmentation’s aim is to identify or construct one or more consumer groups who are
similar with respect to a prede?ned criterion, to learn as much as possible about them,
and – if one or more segments are found to be managerially useful – modify the entire
marketing mix to best cater for the segment/s. The result of successful market
segmentation is competitive advantage in the marketplace due to strong positioning in
a particular part of the market.
A wide range of personal characteristics can be used as prede?ned criteria
(segmentation criteria, segmentation bases) for market segmentation:
socio-demographics (e.g. students versus retired people), behavioral variables (e.g.
repeat visitors versus ?rst time visitors), or psychographic variables (e.g. tourists
interested in the local population versus tourists attending a major sporting event).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1750-6182.htm
Management
learning exercise
289
Received April 2007
Revised May 2007
Accepted June 2007
International Journal of Culture,
Tourism and Hospitality Research
Vol. 1 No. 4, 2007
pp. 289-295
qEmerald Group Publishing Limited
1750-6182
DOI 10.1108/17506180710824172
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Market segments derive in many different ways. Typically, segmentation
approaches are refereed to as either a priori (commonsense) segmentation
approaches (Dolnicar, 2004; Mazanec, 2000) or a posteriori ( post hoc, data-driven)
segmentation approaches (Dolnicar, 2004; Mazanec, 2000; Myers and Tauber, 1977). In
a commonsense segmentation study destination management decides in advance
which personal characteristics will be used to split tourists into segments. A typical
example is to form market segments based on tourists’ country of origin. In a
data-driven segmentation study, multiple variables are used to form market segments.
For instance, a set of ten travel motives or six typical vacation behaviors. These
variables represent the segmentation base and are used to form groups of similar
respondents. The resulting segments have to be interpreted and understood well before
they can be named. They result from an exploratory data-driven process. Often,
cluster-analytic techniques are employed to identify or to construct segments in a
data-driven manner in tourism. A typical example is bene?t segmentation. Dolnicar
(2004) provides a more comprehensive overview of segmentation approaches including
various combinations of commonsense and data-driven techniques.
The following exercise demonstrates how data-driven segmentation can be applied
by any tourism industry entity to explore the marketplace. The exercise takes the
perspective of an Australian tour operator. The tour operator – who specializes in
adventure tours in Australia and the Himalayas – is particularly interested whether or
not a modest price increase would affect demand, and if some categories of adventure
trip tourists may be willing to pay a price premium. The tour operator wants to know
whether the market can be segmented on the basis of willingness to pay a price
premium. If these segmentation categories can be de?ned accurately, the tour operator
can more effectively manage promotion campaigns. Currently, the tour operator uses
two main advertising channels (slide shows and advertisements in newspapers).
Which communication channel is most effective in reaching the customer segment that
is willing to pay a price premium? Finally, do the segments differ in their interest to
travel to different destinations? If yes, can the company develop the most suitable
product for them? The results include a data-driven segmentation solution as well as a
pro?le of each segment; both pieces of information form the basis for the evaluating
managerial usefulness of the derived data-driven segmentation solution. Furthermore,
a number of methodological issues are highlighted which are essential to the correct
implementation of a data-driven segmentation study.
The data
To answer the research question, the tour operator conducted ?eldwork using an
e-mail list of Australian subscribers to an adventure tourism newsletter. The
questionnaire took respondents about 20 minutes to complete. From a list of adventure
travel components, respondents were asked which activities they would be willing to
pay a price premium. Nine variables were used to cover different aspects of willingness
to pay in the context of and adventure trip. Respondents were asked to respond with a
“yes” or “no.” A generous prize incentive was offered to ensure a high-response rate.
The ?nal sample contains 649 respondents.
Respondents also were asked about their intention to undertake adventure travel,
the information sources they used, and their preferred destinations. All questions
required respondents to answer with “yes” or “no” only. Note that choosing the
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“yes-no” format (binary format) was very deliberate given that tourism datasets
frequently contain respondents from different cultures and that ordinal or rating scales
with multiple categories (such as the Likert scale) are known to be susceptible to
response biases which can contaminate the data and consequently put in question the
validity of results (Dolnic?ar and Gru¨n, 2007a, b).
Training exercise for tourism research analysts and executives
The tour operator conducted a segmentation study using a partitioning algorithm
called Topology Representing Network (TRN) (Martinetz and Schulten, 1994). TRN is
similar to the frequently used k-means algorithm; however this algorithm has been
shown to outperform alternative cluster algorithms (including k-means) in a
Monte-Carlo situation with arti?cial data (Buchta et al., 1997). The tour operator
consequently felt con?dent that the algorithm choice was suitable. The underlying
distance measure was Euclidean distance, which is legitimate given that binary data
were used. A total of 50 replications were conducted for each number of segments. The
differences in stability are provided in Table I.
The tour operator concluded that the highest increase in stability occurred when a
four segment solution was computed. The tour operator consequently chose the four
segment solution and computed the ?nal segments – the sizes are reported in Table II.
Figure 1 shows the segment pro?les for all segments.
Finally, the tour operator wanted to validate the resulting segments with additional
information of particular managerial interest. For this purpose, the tour operator
computed x
2
tests because all variables are categorical in nature and because the
number of variables is small enough to permit Bonferroni correction to be used to
account for the overestimation of signi?cance due to independent testing. The test
results are provided in Table III.
Number of
clusters
Number of repeated
calculations
Percent uncertainty
reduction
Improvement in percent
uncertainly reduction
3 50 71.96
4 50 86.14 14.18
5 50 80.00 26.14
6 50 81.86 1.86
7 50 84.86 3.00
8 50 87.47 2.61
Table I.
Stability of solutions
ranging from three
to eight segments
Segment Frequency Percent
1 108 17
2 252 39
3 190 29
4 99 15
Total 649 100
Table II.
Size of segments
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Exercise questions:
(1) Is the segmentation base used suitable to help the tour operator answer the
research question? Could the use of this segmentation base potentially lead to
invalid results?
(2) What type of segmentation analysis did the tour operator perform?
(3) Would you classify the segmentation solution as “true,” “stable,” or
“constructive” clusters? Please justify your decision.
(4) Check whether the data-driven market segmentation was conducted in a
methodologically sound manner, speci?cally with respect to the following aspects:
.
Is the sample size large enough to segment tourists based on nine variables?
Figure 1.
Segment pro?les
(willingness to pay a
premium price for . . .)
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Segment 1
Total
Segment 2
Total
Segment 3
Total
Segment 4
Total
Seg. 1 Seg. 2 Seg. 3 Seg. 4 p-value
Bonferroni-corrected
p-value
Intention to undertake adventure
travel in future 16 39 30 16 0.029 0.200
Information source: slide nights 10 49 28 13 0.000 0.001
Information source: newspapers 12 42 26 20 0.007 0.047
Destination of interest: Australia 16 40 28 16 0.747 5.230
Destination of interest: USA 17 35 23 25 0.001 0.010
Destination of interest: France 16 40 19 25 0.000 0.002
Destination of interest: Bhutan 10 46 32 12 0.002 0.015
Table III.
Validation using
additional variables
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Does the sample limit the amount of insight that can be gained from this
segmentation analysis?
.
Was choosing the four segment solution the correct decision? Would you
recommend investigating another solution in more detail?
.
Given the data format of the segmentation base, was the correct test
employed to validate the results?
(5) Interpret the resulting market segments.
(6) Comment on the managerial usefulness of the resulting market segments.
Instructor’s notes and possible solutions
This exercise aims to:
.
provide an opportunity for students, analysts, and executives to interpret the
results of a typical data-driven segmentation study; and
.
to encourage them to critically question the approach taken in the segmentation
study.
Solutions to exercises include the following comments:
(1) The segmentation base is an interesting choice and possibly the best one the tour
operator could create given that an e-mail survey was conducted. The danger
with this segmentation base is that respondents only stated their willingness to
pay more for speci?c services. Respondents did not actually make the decision to
do so. Perhaps, the answers were affected by social desirability bias or other
response biases.
(2) Data-driven segmentation. But, strictly speaking only adventure travelers were
studied, so the kind of segmentation would be an example of Case 5
segmentation: commonsense segmentation using the kind of vacation, then
data-driven segmentation using stated willingness to pay a price premium
(Dolnicar, 2008, forthcoming).
(3) The stability values in Table I do not indicate a very high level of data structure,
particularly given that the eight-cluster solution does not lead to a high increase
of stability. A reasonable conclusion is that if the four cluster solution represents
true clusters, they would have to be classi?ed as either “stable” or “constructive”.
(4) Method aspects:
.
The sample size is large enough. This concern can be tested using Formann’s
(1984) formula for binary data, whereby the sample size should be at least 2
k
with k representing the number of variables, in our case 2
9
. While 5
*
2
k
respondents would be ideal, the data set ful?lls the basic requirement
(2
9
¼ 512) because the tour operator’s sample contains 649 respondents.
.
Yes, this study was essentially a convenience sample of people who
subscribe to a newsletter and provide their e-mail address to obtain the
newsletter. The sample could be skewed towards more experienced
adventure travelers and younger people.
.
Based on the improvement in stability the four segment solution was a good
choice. Given that data-driven segmentation is always exploratory in nature,
Management
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exploring other solutions would be of interest as to see if they would provide
a more differentiated and therefore more managerially useful solution.
.
Yes, given that all variables are binary categories (willingness to pay, use of
information sources, and interest in different tourist destinations) x
2
tests
may be computed. Bonferroni correction is needed, however, to account for
the fact that interrelations between variables were ignored by conducing
independent x
2
tests.
(5) The four segments resulting from the segmentation solution have very distinct
pro?les. Segment 1 is only willing to pay a price premium for increased comfort.
Segment 2 members would not pay a price premium for additional comfort in
general (e.g. better accommodation or more private transport), however, they are
willing to pay more for traveling to remote areas, areas with capacity
restrictions, smaller group sizes, a good local network, and a high level of
security and health safety. The largest group (40 percent) of adventure travelers
is in this segment. Segment 3, representing one-third of the sample is similar to
Segment 2, except this segment’s members are not willing to pay a price
premium for increased safety, security and health standards. Finally, members
of Segment 4 state that they would be willing to pay a price premiumfor each one
of the listed aspects. This segment must be interpreted with great care because
the results could be a re?ection of acquiescence (yes saying) response style.
The segments are externally valid. Signi?cant differences emerge from the
comparisons of additional pieces of information which were not used to construct
the grouping (Table III). Based on the Bonferroni corrected p-values, all items in
this analysis discriminate between the segments except their intention to
undertake adventure travel and their interest in undertaking an adventure trip in
Australia. These two values are not statistically signi?cant suggesting that
the segments do not differ. The results show that Segments 2 and 3 members
(those segments less concerned with comfort) feel attracted to more exotic places
like Bhutan, whereas Segment 1 members indicate the strongest level of interest
in safe (and comfortable) destinations such as Australia, the USA and France. In
terms of advertising channels, slide nights appear to be most suited to
communicate with members of Segment 2, only a very small proportion of
Segment 1 members can be reached through those channels.
(6) Segments 2 and 3 appear to be the managerially most useful choice for the tour
operator. These segments are very distinct in their willingness to pay a premium
price patters, they both match the strengths of the tour operator with respect to
the destinations they are interested in, a fairly large proportion of both segments
can be communicated with (are reachable) through the standard advertising
channels (slide shows and newspapers), and they represent a signi?cant
proportion of the sample (suitable size). Note that generalizing the results to the
population of adventure tourists is problematic because of the convenience
sample approach. The sampling procedure is biased towards readers of
electronic newsletters. The only criterion that cannot be assessed based on the
above analyses is the identi?ability of Segments 2 and 3 members. Additional
background variables are needed (e.g. age, gender, education, or occupation).
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Conclusions
Market segmentation is a valuable technique to explore the characteristics of parts of
the tourist market, which forms the basis of evaluating market segments and selecting
suitable target markets to cater for and communicate with. The preceding provides an
example of how to conduct a data-driven segmentation study. Based on selected
variables from an empirical data set, a number of alternative segmentation solutions
are computed. The most stable solutions form the basis of interpretation, both with
regard to segment distinctiveness along the actual segmentation base, as well as along
additional personal characteristics contained in the data set.
The case discussion in this paper represents only one of many possible ways of
conducting market segmentation. Students, analysts and executives should be aware
that market segmentation is an exploratory technique that aims at aiding managerial
decision making. Resulting segments are not necessarily naturally occurring distinct
groups. Often, these segments represent the most suitable grouping for managerial
purposes. When data-driven segmentation studies are conducted, careful and informed
decisions must be made about the methodology chosen, as the methodology can have
major impacts on the segmentation results.
References
Buchta, C., Dimitriadou, E., Dolnicar, S., Leisch, F. and Weingessel, A. (1997), “A comparison of
several cluster algorithms on arti?cial binary data scenarios from travel market
segmentation”, Working Paper No. 7, SFB Adaptive Information Systems and Modeling in
Economics and Management Science, Vienna.
Dolnicar, S. (2004), “Beyond ‘commonsense segmentation’ – a systematics of segmentation
approaches in tourism”, Journal of Travel Research, Vol. 42 No. 3, pp. 244-50.
Dolnicar, S. (2008), “Market segmentation in tourism”, in Woodside, A. and Martin, D. (Eds),
Advancing Tourism Management, CABI, Boston, MA, forthcoming.
Dolnic?ar, S. andGru¨n, B. (2007a), “Assessing analytical robustness incross-cultural comparisons”,
International Journal of Tourism, Culture, and Hospitality Research, Vol. 1 No. 2.
Dolnic?ar, S. and Gru¨n, B. (2007b), “Cross-cultural differences in survey response patterns”,
International Marketing Review, Vol. 24 No. 2, pp. 127-43.
Formann, A.K. (1984), Die Latent-Class-Analyse: Einfu¨ hrung in die Theorie und Anwendung,
Beltz, Weinheim.
Martinetz, Th. and Schulten, K. (1994), “Topology representing networks”, Neural Networks,
Vol. 7 No. 5, pp. 507-22.
Mazanec, J.A. (2000), “Market segmentation”, in Jafari, J. (Ed.), Encyclopedia of Tourism,
Routledge, London.
Myers, J.H. and Tauber, E. (1977), Market Structure Analysis, American Marketing Association,
Chicago, IL.
Smith, W. (1956), “Product differentiation and market segmentation as alternative marketing
strategies”, Journal of Marketing, Vol. 21, pp. 3-8.
Corresponding author
Sara Dolnicar can be contacted at: [email protected]
Management
learning exercise
295
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This article has been cited by:
1. Hany Kim, Svetlana Stepchenkova. 2015. Effect of tourist photographs on attitudes towards destination:
Manifest and latent content. Tourism Management 49, 29-41. [CrossRef]
2. Arturo Molina, Mar Gómez, Belén González-Díaz, Águeda Esteban. 2015. Market segmentation in wine
tourism: strategies for wineries and destinations in Spain. Journal of Wine Research 26, 192-224. [CrossRef]
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doc_633404707.pdf
This paper aims to show how researchers can develop learning exercises for training
analysts and executives in market segmentation techniques
International Journal of Culture, Tourism and Hospitality Research
Management learning exercise and trainer's note for market segmentation in tourism
Sara Dolnicar
Article information:
To cite this document:
Sara Dolnicar, (2007),"Management learning exercise and trainer's note for market segmentation in
tourism", International J ournal of Culture, Tourism and Hospitality Research, Vol. 1 Iss 4 pp. 289 - 295
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J ohn T. Bowen, (1998),"Market segmentation in hospitality research: no longer a sequential process",
International J ournal of Contemporary Hospitality Management, Vol. 10 Iss 7 pp. 289-296 http://
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Management learning exercise
and trainer’s note for market
segmentation in tourism
Sara Dolnicar
School of Management andMarketing andMarketing ResearchInnovationCentre,
University of Wollongong, Wollongong, Australia
Abstract
Purpose – This paper aims to show how researchers can develop learning exercises for training
analysts and executives in market segmentation techniques.
Design/methodology/approach – The empirical example of a tour operator specializing in
adventure tourism is used as an illustration. Segments are constructed on the basis of tourists’ stated
willingness to pay a price premium for certain aspects of the tour. Stability analysis is conducted to
choose the number of clusters, topology representing networks are used to construct segments and
Bonferroni-corrected x
2
tests provide insight into the external validity of segments.
Findings – Four market segments are constructed which differ signi?cantly with respect to external
variables.
Research limitations/implications – Market segmentation can be used by any entity in the
tourism industry to select a suitable part of the entire market, customize the tourism service to suit
such a segment, and spend marketing budget more ef?ciently by using communication channels and
advertising messages most effective for the selected segment.
Originality/value – Market segmentation provides managers with insight into market structure.
Knowledge about the market structure, in turn, is the basis of successful strategic planning. While the
concept of segmentation is not new, each application is unique to its context. The present paper
focuses on price premium segments in the adventure tourism context.
Keywords Market segmentation, Learning, Training, Tourism management
Paper type Research paper
Smith (1956, p. 6), who introduced the concept of market segmentation to the ?eld of
marketing, provides the following de?nition for market segmentation: “Market
segmentation [. . .] consists of viewing a heterogeneous market (one characterized by
divergent demand) as a number of smaller homogeneous markets.” Market
segmentation’s aim is to identify or construct one or more consumer groups who are
similar with respect to a prede?ned criterion, to learn as much as possible about them,
and – if one or more segments are found to be managerially useful – modify the entire
marketing mix to best cater for the segment/s. The result of successful market
segmentation is competitive advantage in the marketplace due to strong positioning in
a particular part of the market.
A wide range of personal characteristics can be used as prede?ned criteria
(segmentation criteria, segmentation bases) for market segmentation:
socio-demographics (e.g. students versus retired people), behavioral variables (e.g.
repeat visitors versus ?rst time visitors), or psychographic variables (e.g. tourists
interested in the local population versus tourists attending a major sporting event).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1750-6182.htm
Management
learning exercise
289
Received April 2007
Revised May 2007
Accepted June 2007
International Journal of Culture,
Tourism and Hospitality Research
Vol. 1 No. 4, 2007
pp. 289-295
qEmerald Group Publishing Limited
1750-6182
DOI 10.1108/17506180710824172
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Market segments derive in many different ways. Typically, segmentation
approaches are refereed to as either a priori (commonsense) segmentation
approaches (Dolnicar, 2004; Mazanec, 2000) or a posteriori ( post hoc, data-driven)
segmentation approaches (Dolnicar, 2004; Mazanec, 2000; Myers and Tauber, 1977). In
a commonsense segmentation study destination management decides in advance
which personal characteristics will be used to split tourists into segments. A typical
example is to form market segments based on tourists’ country of origin. In a
data-driven segmentation study, multiple variables are used to form market segments.
For instance, a set of ten travel motives or six typical vacation behaviors. These
variables represent the segmentation base and are used to form groups of similar
respondents. The resulting segments have to be interpreted and understood well before
they can be named. They result from an exploratory data-driven process. Often,
cluster-analytic techniques are employed to identify or to construct segments in a
data-driven manner in tourism. A typical example is bene?t segmentation. Dolnicar
(2004) provides a more comprehensive overview of segmentation approaches including
various combinations of commonsense and data-driven techniques.
The following exercise demonstrates how data-driven segmentation can be applied
by any tourism industry entity to explore the marketplace. The exercise takes the
perspective of an Australian tour operator. The tour operator – who specializes in
adventure tours in Australia and the Himalayas – is particularly interested whether or
not a modest price increase would affect demand, and if some categories of adventure
trip tourists may be willing to pay a price premium. The tour operator wants to know
whether the market can be segmented on the basis of willingness to pay a price
premium. If these segmentation categories can be de?ned accurately, the tour operator
can more effectively manage promotion campaigns. Currently, the tour operator uses
two main advertising channels (slide shows and advertisements in newspapers).
Which communication channel is most effective in reaching the customer segment that
is willing to pay a price premium? Finally, do the segments differ in their interest to
travel to different destinations? If yes, can the company develop the most suitable
product for them? The results include a data-driven segmentation solution as well as a
pro?le of each segment; both pieces of information form the basis for the evaluating
managerial usefulness of the derived data-driven segmentation solution. Furthermore,
a number of methodological issues are highlighted which are essential to the correct
implementation of a data-driven segmentation study.
The data
To answer the research question, the tour operator conducted ?eldwork using an
e-mail list of Australian subscribers to an adventure tourism newsletter. The
questionnaire took respondents about 20 minutes to complete. From a list of adventure
travel components, respondents were asked which activities they would be willing to
pay a price premium. Nine variables were used to cover different aspects of willingness
to pay in the context of and adventure trip. Respondents were asked to respond with a
“yes” or “no.” A generous prize incentive was offered to ensure a high-response rate.
The ?nal sample contains 649 respondents.
Respondents also were asked about their intention to undertake adventure travel,
the information sources they used, and their preferred destinations. All questions
required respondents to answer with “yes” or “no” only. Note that choosing the
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“yes-no” format (binary format) was very deliberate given that tourism datasets
frequently contain respondents from different cultures and that ordinal or rating scales
with multiple categories (such as the Likert scale) are known to be susceptible to
response biases which can contaminate the data and consequently put in question the
validity of results (Dolnic?ar and Gru¨n, 2007a, b).
Training exercise for tourism research analysts and executives
The tour operator conducted a segmentation study using a partitioning algorithm
called Topology Representing Network (TRN) (Martinetz and Schulten, 1994). TRN is
similar to the frequently used k-means algorithm; however this algorithm has been
shown to outperform alternative cluster algorithms (including k-means) in a
Monte-Carlo situation with arti?cial data (Buchta et al., 1997). The tour operator
consequently felt con?dent that the algorithm choice was suitable. The underlying
distance measure was Euclidean distance, which is legitimate given that binary data
were used. A total of 50 replications were conducted for each number of segments. The
differences in stability are provided in Table I.
The tour operator concluded that the highest increase in stability occurred when a
four segment solution was computed. The tour operator consequently chose the four
segment solution and computed the ?nal segments – the sizes are reported in Table II.
Figure 1 shows the segment pro?les for all segments.
Finally, the tour operator wanted to validate the resulting segments with additional
information of particular managerial interest. For this purpose, the tour operator
computed x
2
tests because all variables are categorical in nature and because the
number of variables is small enough to permit Bonferroni correction to be used to
account for the overestimation of signi?cance due to independent testing. The test
results are provided in Table III.
Number of
clusters
Number of repeated
calculations
Percent uncertainty
reduction
Improvement in percent
uncertainly reduction
3 50 71.96
4 50 86.14 14.18
5 50 80.00 26.14
6 50 81.86 1.86
7 50 84.86 3.00
8 50 87.47 2.61
Table I.
Stability of solutions
ranging from three
to eight segments
Segment Frequency Percent
1 108 17
2 252 39
3 190 29
4 99 15
Total 649 100
Table II.
Size of segments
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Exercise questions:
(1) Is the segmentation base used suitable to help the tour operator answer the
research question? Could the use of this segmentation base potentially lead to
invalid results?
(2) What type of segmentation analysis did the tour operator perform?
(3) Would you classify the segmentation solution as “true,” “stable,” or
“constructive” clusters? Please justify your decision.
(4) Check whether the data-driven market segmentation was conducted in a
methodologically sound manner, speci?cally with respect to the following aspects:
.
Is the sample size large enough to segment tourists based on nine variables?
Figure 1.
Segment pro?les
(willingness to pay a
premium price for . . .)
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Segment 1
Total
Segment 2
Total
Segment 3
Total
Segment 4
Total
Seg. 1 Seg. 2 Seg. 3 Seg. 4 p-value
Bonferroni-corrected
p-value
Intention to undertake adventure
travel in future 16 39 30 16 0.029 0.200
Information source: slide nights 10 49 28 13 0.000 0.001
Information source: newspapers 12 42 26 20 0.007 0.047
Destination of interest: Australia 16 40 28 16 0.747 5.230
Destination of interest: USA 17 35 23 25 0.001 0.010
Destination of interest: France 16 40 19 25 0.000 0.002
Destination of interest: Bhutan 10 46 32 12 0.002 0.015
Table III.
Validation using
additional variables
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Does the sample limit the amount of insight that can be gained from this
segmentation analysis?
.
Was choosing the four segment solution the correct decision? Would you
recommend investigating another solution in more detail?
.
Given the data format of the segmentation base, was the correct test
employed to validate the results?
(5) Interpret the resulting market segments.
(6) Comment on the managerial usefulness of the resulting market segments.
Instructor’s notes and possible solutions
This exercise aims to:
.
provide an opportunity for students, analysts, and executives to interpret the
results of a typical data-driven segmentation study; and
.
to encourage them to critically question the approach taken in the segmentation
study.
Solutions to exercises include the following comments:
(1) The segmentation base is an interesting choice and possibly the best one the tour
operator could create given that an e-mail survey was conducted. The danger
with this segmentation base is that respondents only stated their willingness to
pay more for speci?c services. Respondents did not actually make the decision to
do so. Perhaps, the answers were affected by social desirability bias or other
response biases.
(2) Data-driven segmentation. But, strictly speaking only adventure travelers were
studied, so the kind of segmentation would be an example of Case 5
segmentation: commonsense segmentation using the kind of vacation, then
data-driven segmentation using stated willingness to pay a price premium
(Dolnicar, 2008, forthcoming).
(3) The stability values in Table I do not indicate a very high level of data structure,
particularly given that the eight-cluster solution does not lead to a high increase
of stability. A reasonable conclusion is that if the four cluster solution represents
true clusters, they would have to be classi?ed as either “stable” or “constructive”.
(4) Method aspects:
.
The sample size is large enough. This concern can be tested using Formann’s
(1984) formula for binary data, whereby the sample size should be at least 2
k
with k representing the number of variables, in our case 2
9
. While 5
*
2
k
respondents would be ideal, the data set ful?lls the basic requirement
(2
9
¼ 512) because the tour operator’s sample contains 649 respondents.
.
Yes, this study was essentially a convenience sample of people who
subscribe to a newsletter and provide their e-mail address to obtain the
newsletter. The sample could be skewed towards more experienced
adventure travelers and younger people.
.
Based on the improvement in stability the four segment solution was a good
choice. Given that data-driven segmentation is always exploratory in nature,
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exploring other solutions would be of interest as to see if they would provide
a more differentiated and therefore more managerially useful solution.
.
Yes, given that all variables are binary categories (willingness to pay, use of
information sources, and interest in different tourist destinations) x
2
tests
may be computed. Bonferroni correction is needed, however, to account for
the fact that interrelations between variables were ignored by conducing
independent x
2
tests.
(5) The four segments resulting from the segmentation solution have very distinct
pro?les. Segment 1 is only willing to pay a price premium for increased comfort.
Segment 2 members would not pay a price premium for additional comfort in
general (e.g. better accommodation or more private transport), however, they are
willing to pay more for traveling to remote areas, areas with capacity
restrictions, smaller group sizes, a good local network, and a high level of
security and health safety. The largest group (40 percent) of adventure travelers
is in this segment. Segment 3, representing one-third of the sample is similar to
Segment 2, except this segment’s members are not willing to pay a price
premium for increased safety, security and health standards. Finally, members
of Segment 4 state that they would be willing to pay a price premiumfor each one
of the listed aspects. This segment must be interpreted with great care because
the results could be a re?ection of acquiescence (yes saying) response style.
The segments are externally valid. Signi?cant differences emerge from the
comparisons of additional pieces of information which were not used to construct
the grouping (Table III). Based on the Bonferroni corrected p-values, all items in
this analysis discriminate between the segments except their intention to
undertake adventure travel and their interest in undertaking an adventure trip in
Australia. These two values are not statistically signi?cant suggesting that
the segments do not differ. The results show that Segments 2 and 3 members
(those segments less concerned with comfort) feel attracted to more exotic places
like Bhutan, whereas Segment 1 members indicate the strongest level of interest
in safe (and comfortable) destinations such as Australia, the USA and France. In
terms of advertising channels, slide nights appear to be most suited to
communicate with members of Segment 2, only a very small proportion of
Segment 1 members can be reached through those channels.
(6) Segments 2 and 3 appear to be the managerially most useful choice for the tour
operator. These segments are very distinct in their willingness to pay a premium
price patters, they both match the strengths of the tour operator with respect to
the destinations they are interested in, a fairly large proportion of both segments
can be communicated with (are reachable) through the standard advertising
channels (slide shows and newspapers), and they represent a signi?cant
proportion of the sample (suitable size). Note that generalizing the results to the
population of adventure tourists is problematic because of the convenience
sample approach. The sampling procedure is biased towards readers of
electronic newsletters. The only criterion that cannot be assessed based on the
above analyses is the identi?ability of Segments 2 and 3 members. Additional
background variables are needed (e.g. age, gender, education, or occupation).
IJCTHR
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Conclusions
Market segmentation is a valuable technique to explore the characteristics of parts of
the tourist market, which forms the basis of evaluating market segments and selecting
suitable target markets to cater for and communicate with. The preceding provides an
example of how to conduct a data-driven segmentation study. Based on selected
variables from an empirical data set, a number of alternative segmentation solutions
are computed. The most stable solutions form the basis of interpretation, both with
regard to segment distinctiveness along the actual segmentation base, as well as along
additional personal characteristics contained in the data set.
The case discussion in this paper represents only one of many possible ways of
conducting market segmentation. Students, analysts and executives should be aware
that market segmentation is an exploratory technique that aims at aiding managerial
decision making. Resulting segments are not necessarily naturally occurring distinct
groups. Often, these segments represent the most suitable grouping for managerial
purposes. When data-driven segmentation studies are conducted, careful and informed
decisions must be made about the methodology chosen, as the methodology can have
major impacts on the segmentation results.
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Corresponding author
Sara Dolnicar can be contacted at: [email protected]
Management
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This article has been cited by:
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Manifest and latent content. Tourism Management 49, 29-41. [CrossRef]
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tourism: strategies for wineries and destinations in Spain. Journal of Wine Research 26, 192-224. [CrossRef]
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